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在2型糖尿病中预测轻度认知障碍:一种机器学习方法

Fangyi Li^1,2,3,4, Shengyi Zhao^1,2,3, Tianyu Wu^2,3

¹Department of Endocrinology, Endocrine and Metabolic Disease Medical Center, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China.

Journal of diabetes research

|October 29, 2025

概括

机器学习可以准确地预测2型糖尿病 (T2DM) 患者的轻度认知障碍 (MCI). 一种支持向量分类器模型显示出高效率,有助于早期干预糖尿病认知衰退.

科学领域:

神经科学是一个神经科学.
内分泌学在内分泌学.
人工智能的人工智能

背景情况:

2型糖尿病 (T2DM) 是认知障碍的重要危险因素,包括轻度认知障碍 (MCI).
在T2DM患者中早期发现MCI对于有效的干预和认知衰退的管理至关重要.
开发预测模型可以帮助识别T2DM人群中的风险人群.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测2型糖尿病患者的轻度认知障碍 (MCI).
确定与T2DM中MCI风险相关的关键临床和人口预测因素.
评估开发的ML模型在不同人群中的通用性和可靠性.

主要方法:

利用了2074名T2DM参与者的数据集,进行认知评估.
采用统计方法和遗传编程来进行特征选择,以减轻对线性.
训练并评估了使用交叉验证和超参数调整的六种分类模型,包括支持矢量分类器 (SVC).
在外部使用DECODE和NHANES III队列验证模型,并使用SHAP分析进行预测解释.

主要成果:

支持矢量分类器 (SVC) 模型表现出卓越的性能,在内部验证中实现了0.74的AUC.
与DECODE队列的外部验证显示性能改善,AUC为0.80.
确定的主要预测因素包括教育,年龄,糖脂代谢 (GCA指数),静脉血压,eGFR,BMI和糖尿病持续时间.

结论:

开发的基于ML的SVC模型在预测T2DM患者的MCI方面是有效和准确的.
机器学习在T2DM人群中对MCI的早期诊断和风险分层具有显著的潜力.
该模型在外部验证队列中的表现强调了其在识别有认知障碍风险的个体的临床实用性.

关键词:

2型糖尿病是什么? 2型糖尿病是什么?机器学习是机器学习.轻度的认知障碍轻度的认知障碍预测模型预测模型

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